基于主分量分析的数字水印

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Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.     

基于主成分分析的高职学生核心竞争力指标研究

运用主成分分析、权重分析的方法对高职大学生核心竞争力的调查数据进行分析,结果表明:职业基础能力、职业核心能力、身心素质和职业素养是高职学生核心竞争力的构成要素,其权重由高到低依次为职业核心能力、身心素质、职业基础能力和职业素养。     

基于主成分和聚类分析的国内房地产发展评价

运用主成分和聚类分析的方法,对2009年全国31个省、直辖市的房地产发展情况进行了分析,结果表明:不同区域的房地产市场发展差异明显,而同一地区不同省份的发展情况也不尽相同.     

基于非负主成分分析的低维光谱色彩空间表示法(英文)

针对采用主成分分析法进行多光谱数据降维会使重构光谱反射比出现负值的问题, 提出一种非负约束主成分分析法, 并用该法构造低维空间, 实现高维多光谱数据向低维空间的转换. 首先分析主成分分析法产生非光谱数据的原因, 据此对经典主成分分析模型增加非负约束; 然后求出一组线性无关的非负主成分权向量, 用该组向量构造低维空间; 最后用非线性优化技术确定高维数据在该低维空间中的投影值, 实现了高维空间与低维空间的相互转换. 实验结果表明, 新方法能使重构光谱数据在[0, 1]内, 保持了光谱反射比的物理意义, 同时所构造低维空间的精度能与经典主成分分析法保持一致.     

改进的人脸识别主分量分析算法

在应用于人脸识别领域的主分量分析(PCA)算法中,为了降低与外界光照变化相关的特征向量对提取特征的影响,提出了一种改进的主分量分析(MPCA)算法,利用相对应的标准方差对提取的特征矢量元素进行归一化处理.采用耶鲁大学的2个人脸数据库(Yale face database和Yaleface database B)进行了验证,实验结果表明,对于正面人脸和具有小角度姿态变化情况下的人脸,提出方法的性能优于传统的PCA和LDA(线性判别分析)算法,而运算量和PCA算法相同,大大低于LDA算法.     

Time series of EIT measurements and images during lung ventilation based on principal component analysis

The aim of this paper is to propose a useful method for exploring regional ventilation and perfusion in the chest and also separation of pulmonary and cardiac changes.The approach is based on estimating both electrical impedance tomography(EIT) measurements and reconstructed images by means of principal component analysis(PCA).In the experiments in vivo,43 cycles of heart-beat rhythm could be detected by PCA when the volunteer held breath;9 breathing cycles and 50 heart-beat cycles could be detected by PCA ...     

基于主成分分析的重庆市水资源承载力研究

应用主成分分析法初步探讨重庆市水资源承载力的影响因子.将主成分分析法引入重庆市水资源承载力研究中,选取总用水量、水资源总量、人口增长率、水资源利用率、年人均用水量、总供水量、人口总数、城镇化率、平均产水系数、万元GDP用水、农田灌溉亩均用水、人均GDP、总耗水量等13个指标建立评价指标体系,并借助SPSS17.0分析软件,进行指标的标准化和综合化处理.研究表明:通过对重庆市2000~2011年水资源承载力状况进行综合评价来看,人口对水资源造成的压力,水资源的自身状况和社会经济情况是影响重庆市水资源承载力的主要因素.重庆市水资源承载力面临着严峻的挑战.     

基于主成分分析法和AHP的河北省综合竞争力评价研究

运用系统分析的方法构建了河北省综合竞争力评估指标体系与评价模型,并在此基础上,综合运用主成分分析法与层次分析法,对属于中观层面的河北省综合竞争力进行了更加细化的评价剖析,拟对提升河北省竞争力、确定经济发展思路和制定合理的发展战略提供决策依据。     

安徽文化产业竞争力：指标、测算与区域比较

通过建立安徽省文化产业竞争力指标评价体系,采用主成分分析法和聚类分析法对安徽省16个地市文化产业竞争力进行测算、排序和聚类。结果显示,安徽省文化产业竞争力水平分布不均衡,从北到南大致呈现文化产业竞争力逐步增强的趋势,并提出了提升各地市文化产业竞争力的对策和建议。     

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling’s T ² statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.     

主成分分析方法在长江水质评价中的应用

利用主成分分析方法对长江流域的水质进行了评价,通过MATLAB软件计算,对长江流域17个地区的污染状况作出了评价排序,并建立了稳定水质的数学模型,确定干流上的变化,找出了主要污染物高锰酸盐和氨氮的分布地区,分析结果与实际相符,具有一定可靠性和实用性.     

基于聚类分析视角的我国省域经济综合竞争力研究

聚类分析是研究事物分类的基本方法,通过比较各事物之间的性质,将性质相近的归为一类,将性质差别较大的归入不同的类,从而达到认识事物的目的.本文通过建立我国省域经济综合竞争力评价指标体系,采集2005和2006两年的数据,利用主成份分析和聚类分析相结合的方法,对全国31个省份近两年的经济综合竞争力进行聚类评价.通过对聚类结果的分析,进一步探索了我国省域经济综合竞争力的影响因素和内在规律,以期加强对省域经济综合竞争力评价结果客观性和科学性的研究.     

On Common Factor and Principal Component Representations of Data: Implications for Theory and for Confirmatory Replications

Common factor analysis (FA) and principal component analysis (PCA) are commonly used to obtain lower-dimensional representations of matrices of correlations among manifest variables. Whereas some experts argue that differences in results from use of FA and PCA are small and relatively unimportant in empirical studies, the fundamental rationales for the two methods are very different. Here, FA and PCA are contrasted on four key issues: the range of possible dimensional loadings, the range of potential correlations among dimensions, the structure of residual covariances and correlations, and the relation between population parameters and the correlational structures with which they are associated. For decades, experts have emphasized indeterminacies of the FA model, particularly indeterminacy of common factor scores. Determinate in most respects, a heretofore unacknowledged, pernicious indeterminacy of PCA is demonstrated: the indeterminacy between PCA structural representations and the correlational structures from which they are derived. Researchers are often advised to use either FA or PCA in exploratory rounds of data analysis to understand and refine the dimensional structure of a domain before moving to Structural Equation Modeling in later theory-testing, confirmatory, replication studies. Results from the current study suggest that PCA is an unreliable method to use for such purposes and may lead to serious misrepresentation of the structure of a domain. Hence, PCA should never be used if the goal is to understand and represent the latent structure of a domain; only FA techniques should be used for this purpose, as only FA provides reliable structural representations as the basis for confirmatory tests in future studies.     

Inverse displacement analysis of the general six degree-of-freedom serial robot based on optimization method

The paper presents a new solution of inverse displacement analysis of the general six degree-of-freedom serial robot. The inverse displacement analysis of the general serial robot is transformed into a minimization problem and then the optimization method is adopted to solve the nonlinear least squares problem with the analytic form of new Jacobian matrix. In this way, joint variables of the general serial robot can be searched out quickly under the desired precision when positions of the three non-collinear end effector points are given. Compared with the general Newton iterative method, the proposed algorithm can search out the solution when the robot is at the singular configuration and the initial configuration used in the optimization method may also be the singular configuration. So the convergence domain is bigger than that of the general Newton iterative method. Another advantage of the proposed algorithm is that positions of the three non-collinear end effector points are usually much easier to be measured than the orientation of the end effector. The inverse displacement analysis of the general 6R (six-revolute-joint) serial robot is illustrated as an example and the simulation results verify the efficiency of the proposed algorithm. Because the three non-collinear points can be selected at random, the method can be applied to any other types of serial robots.     

Blind separation of speech signals based on wavelet transform and independent component analysis

Speech signals in frequency domain were separated based on discrete wavelet transform （DWT） and inde- pendent component analysis （ICA）. First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation （BSS） were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were car- ried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.     

基于一个新核函数的半定规划的内点算法的复杂性分析

Interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with simple algebraic expression is proposed. Based on this kernel function, a primal-dual interior-point methods （IPMs） for semidefinite optimization （SDO） is designed. And the iteration complexity of the algorithm as O（n^3/4 log n/ε） with large-updates is established. The resulting bound is better than the classical kernel function, with its iteration complexity O（n log n/ε） in large-updates case.     

校园日照分析优化与室外活动场地设计——以福建工程学院北校区为例

以福建工程学院北区校园为例,从日照角度分析,结合校园内主要活动群体外出活动时间规律,通过天正日照分析软件对室外环境进行建筑物阴影轮廓和多点日照分析,了解在不同时点建筑物的阴影范围和户外空间中的日照时长,并以此为依据,对校园室外活动场所的位置选择进行优化,为校区外部活动场所的设计提供指导性的建议。     

基于主成分分析法的重庆市科技竞争力的实证研究

根据科技竞争力评价的一般范式,建立了一套评价指标体系。利用主成分分析方法,从纵向角度对重庆市科技竞争力进行了实证分析。最后,对重庆市提高科技竞争力提出了几点建议。     

Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stul, and panicles infected with Ustilaginoidea virens. Low order derivative sp...